TY - GEN
T1 - Deep Learning-Based Image Recognition Systems for IoT-Driven Predictive Health Care
AU - Dankan Gowda, V.
AU - Sharma, Avinash
AU - Poornima, Galiveeti
AU - Al Said, Nidal
AU - Jagtap, Madan Mohanrao
AU - Saxena, Rini
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - The combination of deep learning and the Internet of Things (IoT) has brought many changes to the prediction of health problems and the provision of solutions for making a timely diagnosis to patients. This paper proposes a new system to connect the IoT data acquisition method with a deep learning image recognition paradigm to diagnose medical conditions at an early stage without a properly labeled dataset. Medical images are classified using convolutional neural networks (CNNs) where the classification performance is measured by precision and recall as well as F1-score which stand at 92%, 88%, and 90%, respectively. The system also plays a role in guaranteeing real-time monitoring using IoT devices, with data transfer indeed taking 1.2 s only as oppose to the traditional approach. Quantitative assessments support the effectiveness of the proposed approach, and the solution solves important problems of traditional health care, including diagnostic procrastination and the lack of scalability. Outcomes of classification and comparative latency are graphically represented to present instant and real-time performance of the system. This research lays down groundwork to apply the advanced technologies into the healthcare structures with an objective of early identification of the issues and efficient working of the patient care system.
AB - The combination of deep learning and the Internet of Things (IoT) has brought many changes to the prediction of health problems and the provision of solutions for making a timely diagnosis to patients. This paper proposes a new system to connect the IoT data acquisition method with a deep learning image recognition paradigm to diagnose medical conditions at an early stage without a properly labeled dataset. Medical images are classified using convolutional neural networks (CNNs) where the classification performance is measured by precision and recall as well as F1-score which stand at 92%, 88%, and 90%, respectively. The system also plays a role in guaranteeing real-time monitoring using IoT devices, with data transfer indeed taking 1.2 s only as oppose to the traditional approach. Quantitative assessments support the effectiveness of the proposed approach, and the solution solves important problems of traditional health care, including diagnostic procrastination and the lack of scalability. Outcomes of classification and comparative latency are graphically represented to present instant and real-time performance of the system. This research lays down groundwork to apply the advanced technologies into the healthcare structures with an objective of early identification of the issues and efficient working of the patient care system.
KW - Artificial intelligence (AI)
KW - Convolutional neural networks (CNNs)
KW - Deep learning
KW - Image recognition
KW - IoT
KW - Medical imaging
KW - Predictive health care
KW - Smart healthcare systems
UR - https://www.scopus.com/pages/publications/105020021520
U2 - 10.1007/978-981-96-7289-9_46
DO - 10.1007/978-981-96-7289-9_46
M3 - Conference contribution
AN - SCOPUS:105020021520
SN - 9789819672882
T3 - Lecture Notes in Networks and Systems
SP - 579
EP - 589
BT - Universal Threats in Expert Applications and Solutions - Proceedings of 4th UNI-TEAS 2025
A2 - Rathore, Vijay Singh
A2 - Tavares, Joao Manuel R. S.
A2 - Hanumanthappa, M.
A2 - Surendiran, B.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2025
Y2 - 1 February 2025 through 4 February 2025
ER -